Accession Number : ADA328266

Title :   Optimal Line Fitting Using Genetic Algorithms.

Descriptive Note : Technical rept.,

Corporate Author : PENNSYLVANIA STATE UNIV UNIVERSITY PARK CENTER FOR MULTIVARIATE ANALYSIS

Personal Author(s) : Pittman, Jennifer ; Murthy, C. A.

PDF Url : ADA328266

Report Date : JUL 1997

Pagination or Media Count : 29

Abstract : Genetic algorithms are computational techniques which, given an optimization problem, use elements of directed and stochastic search to find the 'best' solution from the space of potential solutions. We apply GA's to the problem of fitting the minimum least-squares piecewise linear function to a set of data points in R(2) . We assume that the number of pieces is known but the knot locations are unknown. The effectiveness of our algorithm is demonstrated with two examples. Results are found to be quite promising and encourage further research.

Descriptors :   *ALGORITHMS, *LEAST SQUARES METHOD, DATA BASES, MATHEMATICAL MODELS, NEURAL NETS, OPTIMIZATION, STOCHASTIC PROCESSES, DATA MANAGEMENT, MULTIVARIATE ANALYSIS, APPROXIMATION(MATHEMATICS), CUBIC SPLINE TECHNIQUE, CURVE FITTING.

Subject Categories : Numerical Mathematics
      Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE